IMR Press / JIN / Volume 22 / Issue 6 / DOI: 10.31083/j.jin2206138
Open Access Original Research
Bioinformatic Analysis and Experimental Validation of Ubiquitin-Proteasomal System-Related Hub Genes as Novel Biomarkers for Alzheimer's Disease
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1 School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 510006 Guangzhou, Guangdong, China
2 The Second School of Clinical Medicine, Southern Medical University, 510515 Guangzhou, Guangdong, China
3 Department of Traditional Chinese Medicine, Guangdong Second Provincial General Hospital, 510310 Guangzhou, Guangdong, China
*Correspondence: zanj@gdut.edu.cn (Jie Zan); zhenglinbo1987@163.com (Linbo Zheng)
These authors contributed equally.
J. Integr. Neurosci. 2023, 22(6), 138; https://doi.org/10.31083/j.jin2206138
Submitted: 14 May 2023 | Revised: 11 June 2023 | Accepted: 19 June 2023 | Published: 18 October 2023
Copyright: © 2023 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
Abstract

Background: Alzheimer’s disease (AD) is a common progressive neurodegenerative disease. The Ubiquitin-Protease system (UPS), which plays important roles in maintaining protein homeostasis in eukaryotic cells, is involved in the development of AD. This study sought to identify differential UPS-related genes (UPGs) in AD patients by using bioinformatic methods, reveal potential biomarkers for early detection of AD, and investigate the association between the identified biomarkers and immune cell infiltration in AD. Methods: The differentially expressed UPGs were screened with bioinformatics analyses using the Gene Expression Omnibus (GEO) database. A weighted gene co-expression network analysis (WGCNA) analysis was performed to explore the key gene modules associated with AD. A Single-sample Gene Set Enrichment Analysis (ssGSEA) analysis was peformed to explore the patterns of immune cells in the brain tissue of AD patients. Real-time quantitative PCR (RT-qPCR) was performed to examine the expression of hub genes in blood samples from healthy controls and AD patients. Results: In this study, we identified four UPGs (USP3, HECW2, PSMB7, and UBE2V1) using multiple bioinformatic analyses. Furthermore, three UPGs (USP3, HECW2, PSMB7) that are strongly correlated with the clinical features of AD were used to construct risk score prediction markers to diagnose and predict the severity of AD. Subsequently, we analyzed the patterns of immune cells in the brain tissue of AD patients and the associations between immune cells and the three key UPGs. Finally, the risk score model was verified in several datasets of AD and showed good accuracy. Conclusions: Three key UPGs are identified as potential biomarker for AD patients. These genes may provide new targets for the early identification of AD patients.

Keywords
AD
machine learning
Ubiquitin-Protease
biomarkers
bioinformatic
Funding
202201010966/Science and Technology Planning Project of Guangzhou
GDKTP2021003800/Science and Technology Commissioner Project of Guangdong Province
2021271/The fifth batch of national TCM clinical outstanding talents training project
20231017/Scientific Research Project of Guangdong Provincial Bureau of Traditional Chinese Medicine
Figures
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